Today, handwriting is becoming an increasingly popular method for inputting data to data handling units, especially to mobile phones and Personal Digital Assistants (PDAs). In order to handle the input data, the handwriting must be recognized and interpreted. Most existing methods for recognizing handwriting require that the characters that are to be inputted are written one by one and are separately recognized. An example of such a method is provided in U.S. Pat. No. 4,731,857, but the most famous is Graffiti®, manufactured by Palm, Inc.
In order to speed up input of data it is desired that cursive handwriting is allowed. Today there are a couple of commercial systems allowing cursive handwriting. Since the recognition of cursive handwriting is far more complex than recognition of separate characters most commercial systems of today employ complicated statistical systems using neural networks and hidden Markov models with integrated dictionaries.
However, one of the drawbacks of the above mentioned systems is that they require high computational power. Further, the systems require large training sets and are highly dependent on the dictionary used.